path: "tensorflow.keras.metrics"
tf_module {
  member {
    name: "AUC"
    mtype: "<type \'type\'>"
  }
  member {
    name: "Accuracy"
    mtype: "<type \'type\'>"
  }
  member {
    name: "BinaryAccuracy"
    mtype: "<type \'type\'>"
  }
  member {
    name: "BinaryCrossentropy"
    mtype: "<type \'type\'>"
  }
  member {
    name: "CategoricalAccuracy"
    mtype: "<type \'type\'>"
  }
  member {
    name: "CategoricalCrossentropy"
    mtype: "<type \'type\'>"
  }
  member {
    name: "CategoricalHinge"
    mtype: "<type \'type\'>"
  }
  member {
    name: "CosineSimilarity"
    mtype: "<type \'type\'>"
  }
  member {
    name: "FalseNegatives"
    mtype: "<type \'type\'>"
  }
  member {
    name: "FalsePositives"
    mtype: "<type \'type\'>"
  }
  member {
    name: "Hinge"
    mtype: "<type \'type\'>"
  }
  member {
    name: "KLDivergence"
    mtype: "<type \'type\'>"
  }
  member {
    name: "LogCoshError"
    mtype: "<type \'type\'>"
  }
  member {
    name: "Mean"
    mtype: "<type \'type\'>"
  }
  member {
    name: "MeanAbsoluteError"
    mtype: "<type \'type\'>"
  }
  member {
    name: "MeanAbsolutePercentageError"
    mtype: "<type \'type\'>"
  }
  member {
    name: "MeanIoU"
    mtype: "<type \'type\'>"
  }
  member {
    name: "MeanRelativeError"
    mtype: "<type \'type\'>"
  }
  member {
    name: "MeanSquaredError"
    mtype: "<type \'type\'>"
  }
  member {
    name: "MeanSquaredLogarithmicError"
    mtype: "<type \'type\'>"
  }
  member {
    name: "MeanTensor"
    mtype: "<type \'type\'>"
  }
  member {
    name: "Metric"
    mtype: "<type \'type\'>"
  }
  member {
    name: "Poisson"
    mtype: "<type \'type\'>"
  }
  member {
    name: "Precision"
    mtype: "<type \'type\'>"
  }
  member {
    name: "Recall"
    mtype: "<type \'type\'>"
  }
  member {
    name: "RootMeanSquaredError"
    mtype: "<type \'type\'>"
  }
  member {
    name: "SensitivityAtSpecificity"
    mtype: "<type \'type\'>"
  }
  member {
    name: "SparseCategoricalAccuracy"
    mtype: "<type \'type\'>"
  }
  member {
    name: "SparseCategoricalCrossentropy"
    mtype: "<type \'type\'>"
  }
  member {
    name: "SparseTopKCategoricalAccuracy"
    mtype: "<type \'type\'>"
  }
  member {
    name: "SpecificityAtSensitivity"
    mtype: "<type \'type\'>"
  }
  member {
    name: "SquaredHinge"
    mtype: "<type \'type\'>"
  }
  member {
    name: "Sum"
    mtype: "<type \'type\'>"
  }
  member {
    name: "TopKCategoricalAccuracy"
    mtype: "<type \'type\'>"
  }
  member {
    name: "TrueNegatives"
    mtype: "<type \'type\'>"
  }
  member {
    name: "TruePositives"
    mtype: "<type \'type\'>"
  }
  member_method {
    name: "KLD"
    argspec: "args=[\'y_true\', \'y_pred\'], varargs=None, keywords=None, defaults=None"
  }
  member_method {
    name: "MAE"
    argspec: "args=[\'y_true\', \'y_pred\'], varargs=None, keywords=None, defaults=None"
  }
  member_method {
    name: "MAPE"
    argspec: "args=[\'y_true\', \'y_pred\'], varargs=None, keywords=None, defaults=None"
  }
  member_method {
    name: "MSE"
    argspec: "args=[\'y_true\', \'y_pred\'], varargs=None, keywords=None, defaults=None"
  }
  member_method {
    name: "MSLE"
    argspec: "args=[\'y_true\', \'y_pred\'], varargs=None, keywords=None, defaults=None"
  }
  member_method {
    name: "binary_accuracy"
    argspec: "args=[\'y_true\', \'y_pred\', \'threshold\'], varargs=None, keywords=None, defaults=[\'0.5\'], "
  }
  member_method {
    name: "binary_crossentropy"
    argspec: "args=[\'y_true\', \'y_pred\', \'from_logits\', \'label_smoothing\'], varargs=None, keywords=None, defaults=[\'False\', \'0\'], "
  }
  member_method {
    name: "categorical_accuracy"
    argspec: "args=[\'y_true\', \'y_pred\'], varargs=None, keywords=None, defaults=None"
  }
  member_method {
    name: "categorical_crossentropy"
    argspec: "args=[\'y_true\', \'y_pred\', \'from_logits\', \'label_smoothing\'], varargs=None, keywords=None, defaults=[\'False\', \'0\'], "
  }
  member_method {
    name: "deserialize"
    argspec: "args=[\'config\', \'custom_objects\'], varargs=None, keywords=None, defaults=[\'None\'], "
  }
  member_method {
    name: "get"
    argspec: "args=[\'identifier\'], varargs=None, keywords=None, defaults=None"
  }
  member_method {
    name: "hinge"
    argspec: "args=[\'y_true\', \'y_pred\'], varargs=None, keywords=None, defaults=None"
  }
  member_method {
    name: "kld"
    argspec: "args=[\'y_true\', \'y_pred\'], varargs=None, keywords=None, defaults=None"
  }
  member_method {
    name: "kullback_leibler_divergence"
    argspec: "args=[\'y_true\', \'y_pred\'], varargs=None, keywords=None, defaults=None"
  }
  member_method {
    name: "mae"
    argspec: "args=[\'y_true\', \'y_pred\'], varargs=None, keywords=None, defaults=None"
  }
  member_method {
    name: "mape"
    argspec: "args=[\'y_true\', \'y_pred\'], varargs=None, keywords=None, defaults=None"
  }
  member_method {
    name: "mean_absolute_error"
    argspec: "args=[\'y_true\', \'y_pred\'], varargs=None, keywords=None, defaults=None"
  }
  member_method {
    name: "mean_absolute_percentage_error"
    argspec: "args=[\'y_true\', \'y_pred\'], varargs=None, keywords=None, defaults=None"
  }
  member_method {
    name: "mean_squared_error"
    argspec: "args=[\'y_true\', \'y_pred\'], varargs=None, keywords=None, defaults=None"
  }
  member_method {
    name: "mean_squared_logarithmic_error"
    argspec: "args=[\'y_true\', \'y_pred\'], varargs=None, keywords=None, defaults=None"
  }
  member_method {
    name: "mse"
    argspec: "args=[\'y_true\', \'y_pred\'], varargs=None, keywords=None, defaults=None"
  }
  member_method {
    name: "msle"
    argspec: "args=[\'y_true\', \'y_pred\'], varargs=None, keywords=None, defaults=None"
  }
  member_method {
    name: "poisson"
    argspec: "args=[\'y_true\', \'y_pred\'], varargs=None, keywords=None, defaults=None"
  }
  member_method {
    name: "serialize"
    argspec: "args=[\'metric\'], varargs=None, keywords=None, defaults=None"
  }
  member_method {
    name: "sparse_categorical_accuracy"
    argspec: "args=[\'y_true\', \'y_pred\'], varargs=None, keywords=None, defaults=None"
  }
  member_method {
    name: "sparse_categorical_crossentropy"
    argspec: "args=[\'y_true\', \'y_pred\', \'from_logits\', \'axis\'], varargs=None, keywords=None, defaults=[\'False\', \'-1\'], "
  }
  member_method {
    name: "sparse_top_k_categorical_accuracy"
    argspec: "args=[\'y_true\', \'y_pred\', \'k\'], varargs=None, keywords=None, defaults=[\'5\'], "
  }
  member_method {
    name: "squared_hinge"
    argspec: "args=[\'y_true\', \'y_pred\'], varargs=None, keywords=None, defaults=None"
  }
  member_method {
    name: "top_k_categorical_accuracy"
    argspec: "args=[\'y_true\', \'y_pred\', \'k\'], varargs=None, keywords=None, defaults=[\'5\'], "
  }
}